Overview

Dataset statistics

Number of variables13
Number of observations36725
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory112.0 B

Variable types

NUM13

Reproduction

Analysis started2021-02-16 22:32:31.871921
Analysis finished2021-02-16 22:33:50.926863
Duration1 minute and 19.05 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

TEY is highly correlated with GTEP and 2 other fieldsHigh correlation
GTEP is highly correlated with TEY and 1 other fieldsHigh correlation
TIT is highly correlated with TEY and 1 other fieldsHigh correlation
CDP is highly correlated with GTEP and 2 other fieldsHigh correlation

Variables

AT
Real number (ℝ)

Distinct count22522
Unique (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.711768802614017
Minimum-6.2348
Maximum37.103
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:51.144821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.2348
5-th percentile5.75548
Q111.78
median17.798
Q323.664
95-th percentile29.4856
Maximum37.103
Range43.3378
Interquartile range (IQR)11.884

Descriptive statistics

Standard deviation7.447413781
Coefficient of variation (CV)0.420478263
Kurtosis-0.8264298693
Mean17.7117688
Median Absolute Deviation (MAD)5.945
Skewness-0.04322413777
Sum650464.7093
Variance55.46397202
2021-02-16T22:33:51.341588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
23.9698< 0.1%
 
18.5258< 0.1%
 
11.928< 0.1%
 
20.727< 0.1%
 
27.7827< 0.1%
 
11.4217< 0.1%
 
19.2637< 0.1%
 
25.5977< 0.1%
 
16.7927< 0.1%
 
18.4317< 0.1%
 
Other values (22512)3665299.8%
 
ValueCountFrequency (%) 
-6.23481< 0.1%
 
-6.04211< 0.1%
 
-5.97931< 0.1%
 
-5.90311< 0.1%
 
-5.89561< 0.1%
 
ValueCountFrequency (%) 
37.1031< 0.1%
 
37.0981< 0.1%
 
36.2641< 0.1%
 
35.8221< 0.1%
 
35.4611< 0.1%
 

AP
Real number (ℝ≥0)

Distinct count791
Unique (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.0710924438395
Minimum985.85
Maximum1036.6
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:51.555027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum985.85
5-th percentile1003.3
Q11008.8
median1012.6
Q31017
95-th percentile1024.3
Maximum1036.6
Range50.75
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation6.462721886
Coefficient of variation (CV)0.006379336983
Kurtosis0.4425953757
Mean1013.071092
Median Absolute Deviation (MAD)4.1
Skewness0.1937108331
Sum37205035.87
Variance41.76677418
2021-02-16T22:33:51.770472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1012.12970.8%
 
1010.82880.8%
 
1011.92840.8%
 
1011.82840.8%
 
1011.12830.8%
 
1012.22810.8%
 
1010.92790.8%
 
1012.62760.8%
 
10122760.8%
 
1012.72750.7%
 
Other values (781)3390292.3%
 
ValueCountFrequency (%) 
985.851< 0.1%
 
986.161< 0.1%
 
986.251< 0.1%
 
986.412< 0.1%
 
986.431< 0.1%
 
ValueCountFrequency (%) 
1036.61< 0.1%
 
1036.52< 0.1%
 
1036.42< 0.1%
 
1036.34< 0.1%
 
1036.21< 0.1%
 

AH
Real number (ℝ≥0)

Distinct count25708
Unique (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.86382020422056
Minimum24.085
Maximum100.2
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:52.029603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24.085
5-th percentile50.7764
Q168.181
median80.465
Q389.373
95-th percentile97.3938
Maximum100.2
Range76.115
Interquartile range (IQR)21.192

Descriptive statistics

Standard deviation14.46118518
Coefficient of variation (CV)0.1857240646
Kurtosis-0.2747524983
Mean77.8638202
Median Absolute Deviation (MAD)10.164
Skewness-0.6278072966
Sum2859548.797
Variance209.1258767
2021-02-16T22:33:52.296804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100.12460.1%
 
100.14460.1%
 
100.16420.1%
 
100.15420.1%
 
100.11380.1%
 
100.09340.1%
 
100.13330.1%
 
100.1270.1%
 
100.17270.1%
 
100.06250.1%
 
Other values (25698)3636599.0%
 
ValueCountFrequency (%) 
24.0851< 0.1%
 
24.6661< 0.1%
 
25.9871< 0.1%
 
26.6151< 0.1%
 
27.5041< 0.1%
 
ValueCountFrequency (%) 
100.24< 0.1%
 
100.191< 0.1%
 
100.185< 0.1%
 
100.17270.1%
 
100.16420.1%
 

AFDP
Real number (ℝ≥0)

Distinct count20495
Unique (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9254114989788973
Minimum2.0874
Maximum7.6106
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:52.599540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.0874
5-th percentile2.6666
Q13.3555
median3.9375
Q34.3768
95-th percentile5.31154
Maximum7.6106
Range5.5232
Interquartile range (IQR)1.0213

Descriptive statistics

Standard deviation0.7739494998
Coefficient of variation (CV)0.1971639152
Kurtosis0.2249146866
Mean3.925411499
Median Absolute Deviation (MAD)0.495
Skewness0.3813059088
Sum144160.7373
Variance0.5989978283
2021-02-16T22:33:52.849933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.00769< 0.1%
 
3.21159< 0.1%
 
4.13648< 0.1%
 
4.10838< 0.1%
 
3.70568< 0.1%
 
4.15938< 0.1%
 
3.86618< 0.1%
 
4.23228< 0.1%
 
4.258< 0.1%
 
3.70438< 0.1%
 
Other values (20485)3664399.8%
 
ValueCountFrequency (%) 
2.08741< 0.1%
 
2.09921< 0.1%
 
2.10571< 0.1%
 
2.11971< 0.1%
 
2.13951< 0.1%
 
ValueCountFrequency (%) 
7.61061< 0.1%
 
7.55491< 0.1%
 
7.31891< 0.1%
 
7.23991< 0.1%
 
6.98311< 0.1%
 

GTEP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count12967
Unique (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.563620204220555
Minimum17.698
Maximum40.716
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:53.110083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17.698
5-th percentile19.251
Q123.128
median25.105
Q329.059
95-th percentile32.9
Maximum40.716
Range23.018
Interquartile range (IQR)5.931

Descriptive statistics

Standard deviation4.19618398
Coefficient of variation (CV)0.1641467033
Kurtosis-0.653899781
Mean25.5636202
Median Absolute Deviation (MAD)2.489
Skewness0.3290851814
Sum938823.952
Variance17.60796
2021-02-16T22:33:53.329625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
24.30818< 0.1%
 
25.46316< 0.1%
 
25.48715< 0.1%
 
25.18415< 0.1%
 
25.10615< 0.1%
 
25.44314< 0.1%
 
25.54714< 0.1%
 
25.35214< 0.1%
 
25.29914< 0.1%
 
25.00614< 0.1%
 
Other values (12957)3657699.6%
 
ValueCountFrequency (%) 
17.6981< 0.1%
 
17.7191< 0.1%
 
17.7381< 0.1%
 
17.7411< 0.1%
 
17.7611< 0.1%
 
ValueCountFrequency (%) 
40.7161< 0.1%
 
40.1061< 0.1%
 
39.371< 0.1%
 
38.9221< 0.1%
 
38.3621< 0.1%
 

TIT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count799
Unique (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081.4275343771274
Minimum1000.8
Maximum1100.9
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:53.568270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000.8
5-th percentile1047.8
Q11071.8
median1085.9
Q31097
95-th percentile1100.1
Maximum1100.9
Range100.1
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation17.5373411
Coefficient of variation (CV)0.01621684352
Kurtosis-0.0459699805
Mean1081.427534
Median Absolute Deviation (MAD)12.9
Skewness-0.8882992558
Sum39715426.2
Variance307.5583329
2021-02-16T22:33:53.785663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
110027357.4%
 
1099.921585.9%
 
1100.113223.6%
 
1099.88702.4%
 
1100.25271.4%
 
1099.73240.9%
 
1100.32600.7%
 
1099.61860.5%
 
1085.41430.4%
 
1086.51370.4%
 
Other values (789)2806376.4%
 
ValueCountFrequency (%) 
1000.81< 0.1%
 
1001.31< 0.1%
 
1001.42< 0.1%
 
1002.91< 0.1%
 
1006.51< 0.1%
 
ValueCountFrequency (%) 
1100.91< 0.1%
 
1100.81< 0.1%
 
1100.71< 0.1%
 
1100.63< 0.1%
 
1100.515< 0.1%
 

TAT
Real number (ℝ≥0)

Distinct count2769
Unique (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.1580057181757
Minimum511.04
Maximum550.61
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:54.024227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum511.04
5-th percentile529.96
Q1544.72
median549.88
Q3550.04
95-th percentile550.3
Maximum550.61
Range39.57
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation6.842985176
Coefficient of variation (CV)0.01252931405
Kurtosis2.015517862
Mean546.1580057
Median Absolute Deviation (MAD)0.26
Skewness-1.755602605
Sum20057652.76
Variance46.82644612
2021-02-16T22:33:54.266107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
550.016571.8%
 
5506481.8%
 
549.986391.7%
 
549.996281.7%
 
549.966251.7%
 
550.046111.7%
 
549.976071.7%
 
550.025901.6%
 
550.035901.6%
 
549.945841.6%
 
Other values (2759)3054683.2%
 
ValueCountFrequency (%) 
511.041< 0.1%
 
512.451< 0.1%
 
512.62< 0.1%
 
513.061< 0.1%
 
513.091< 0.1%
 
ValueCountFrequency (%) 
550.611< 0.1%
 
550.61< 0.1%
 
550.591< 0.1%
 
550.572< 0.1%
 
550.563< 0.1%
 

TEY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count6236
Unique (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.50629952348535
Minimum100.02
Maximum179.5
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:54.536394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100.02
5-th percentile109.03
Q1124.45
median133.73
Q3144.08
95-th percentile161.33
Maximum179.5
Range79.48
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation15.61971382
Coefficient of variation (CV)0.1169960809
Kurtosis-0.5003798711
Mean133.5062995
Median Absolute Deviation (MAD)9.76
Skewness0.1165443689
Sum4903018.85
Variance243.9754599
2021-02-16T22:33:54.803086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
133.781850.5%
 
133.741740.5%
 
133.761680.5%
 
133.671630.4%
 
133.791490.4%
 
133.721450.4%
 
133.751410.4%
 
133.731400.4%
 
133.771360.4%
 
133.681350.4%
 
Other values (6226)3518995.8%
 
ValueCountFrequency (%) 
100.021< 0.1%
 
100.031< 0.1%
 
100.041< 0.1%
 
100.071< 0.1%
 
100.141< 0.1%
 
ValueCountFrequency (%) 
179.51< 0.1%
 
178.311< 0.1%
 
177.911< 0.1%
 
177.881< 0.1%
 
177.491< 0.1%
 

CDP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count4447
Unique (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.060486159292036
Minimum9.8518
Maximum15.159
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:55.065729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9.8518
5-th percentile10.385
Q111.434
median11.965
Q312.854
95-th percentile13.989
Maximum15.159
Range5.3072
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.088871023
Coefficient of variation (CV)0.09028417332
Kurtosis-0.6317523436
Mean12.06048616
Median Absolute Deviation (MAD)0.637
Skewness0.2368475605
Sum442921.3542
Variance1.185640104
2021-02-16T22:33:55.284616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.891550.1%
 
11.872470.1%
 
11.908460.1%
 
11.902450.1%
 
11.899440.1%
 
11.839430.1%
 
11.916430.1%
 
11.901430.1%
 
11.905410.1%
 
11.886410.1%
 
Other values (4437)3627798.8%
 
ValueCountFrequency (%) 
9.85181< 0.1%
 
9.87081< 0.1%
 
9.87541< 0.1%
 
9.88061< 0.1%
 
9.90441< 0.1%
 
ValueCountFrequency (%) 
15.1591< 0.1%
 
15.0831< 0.1%
 
15.0811< 0.1%
 
15.0551< 0.1%
 
15.0431< 0.1%
 

CO
Real number (ℝ≥0)

Distinct count26184
Unique (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3723655245012933
Minimum0.00038751
Maximum44.103
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:55.516944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00038751
5-th percentile0.468172
Q11.1823
median1.7137
Q32.8429
95-th percentile6.1356
Maximum44.103
Range44.10261249
Interquartile range (IQR)1.6606

Descriptive statistics

Standard deviation2.262331685
Coefficient of variation (CV)0.9536185137
Kurtosis49.11638345
Mean2.372365525
Median Absolute Deviation (MAD)0.6741
Skewness4.839470838
Sum87125.12389
Variance5.118144655
2021-02-16T22:33:55.747473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.11124210.1%
 
1.33738< 0.1%
 
1.3568< 0.1%
 
1.68978< 0.1%
 
1.53957< 0.1%
 
1.81937< 0.1%
 
1.73577< 0.1%
 
1.69387< 0.1%
 
1.87987< 0.1%
 
1.33487< 0.1%
 
Other values (26174)3663899.8%
 
ValueCountFrequency (%) 
0.000387511< 0.1%
 
0.00159351< 0.1%
 
0.00163981< 0.1%
 
0.00366531< 0.1%
 
0.00503341< 0.1%
 
ValueCountFrequency (%) 
44.1031< 0.1%
 
43.6221< 0.1%
 
43.4281< 0.1%
 
43.3971< 0.1%
 
41.0971< 0.1%
 

NOX
Real number (ℝ≥0)

Distinct count23636
Unique (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.29486453369638
Minimum25.905
Maximum119.91
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:55.985177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25.905
5-th percentile49.293
Q157.164
median63.851
Q371.549
95-th percentile85.1878
Maximum119.91
Range94.005
Interquartile range (IQR)14.385

Descriptive statistics

Standard deviation11.67530938
Coefficient of variation (CV)0.1788089991
Kurtosis2.037130543
Mean65.29486453
Median Absolute Deviation (MAD)7.132
Skewness1.026394052
Sum2397953.9
Variance136.312849
2021-02-16T22:33:56.174514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
60.381040.3%
 
64.0969< 0.1%
 
64.2998< 0.1%
 
64.1098< 0.1%
 
64.1057< 0.1%
 
66.8617< 0.1%
 
59.3547< 0.1%
 
61.5797< 0.1%
 
56.777< 0.1%
 
66.4127< 0.1%
 
Other values (23626)3655499.5%
 
ValueCountFrequency (%) 
25.9051< 0.1%
 
27.1831< 0.1%
 
27.7651< 0.1%
 
29.0631< 0.1%
 
35.5981< 0.1%
 
ValueCountFrequency (%) 
119.911< 0.1%
 
119.91< 0.1%
 
119.891< 0.1%
 
119.791< 0.1%
 
119.681< 0.1%
 

year
Real number (ℝ≥0)

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.9854867256638
Minimum2011
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:56.366465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2015
Maximum2015
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.419011996
Coefficient of variation (CV)0.0007049290744
Kurtosis-1.310674059
Mean2012.985487
Median Absolute Deviation (MAD)1
Skewness0.02400505968
Sum73926892
Variance2.013595045
2021-02-16T22:33:56.595238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2012762820.8%
 
2011741120.2%
 
2015738320.1%
 
2013715219.5%
 
2014715119.5%
 
ValueCountFrequency (%) 
2011741120.2%
 
2012762820.8%
 
2013715219.5%
 
2014715119.5%
 
2015738320.1%
 
ValueCountFrequency (%) 
2015738320.1%
 
2014715119.5%
 
2013715219.5%
 
2012762820.8%
 
2011741120.2%
 

target
Real number (ℝ≥0)

Distinct count6236
Unique (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.5060473791695
Minimum100.02
Maximum179.5
Zeros0
Zeros (%)0.0%
Memory size286.9 KiB
2021-02-16T22:33:56.863559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100.02
5-th percentile109.03
Q1124.45
median133.73
Q3144.08
95-th percentile161.33
Maximum179.5
Range79.48
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation15.61976978
Coefficient of variation (CV)0.116996721
Kurtosis-0.5004061996
Mean133.5060474
Median Absolute Deviation (MAD)9.76
Skewness0.1165877433
Sum4903009.59
Variance243.9772079
2021-02-16T22:33:57.137995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
133.781850.5%
 
133.741740.5%
 
133.761680.5%
 
133.671630.4%
 
133.791490.4%
 
133.721450.4%
 
133.751410.4%
 
133.731400.4%
 
133.771360.4%
 
133.681350.4%
 
Other values (6226)3518995.8%
 
ValueCountFrequency (%) 
100.021< 0.1%
 
100.031< 0.1%
 
100.041< 0.1%
 
100.071< 0.1%
 
100.141< 0.1%
 
ValueCountFrequency (%) 
179.51< 0.1%
 
178.311< 0.1%
 
177.911< 0.1%
 
177.881< 0.1%
 
177.491< 0.1%
 

Interactions

2021-02-16T22:32:38.762824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:39.055408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:32:39.703105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:40.025553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:40.321550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:40.642493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:41.001468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:41.578616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:42.082175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:42.529281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:42.948131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:43.413519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:43.809448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:44.167329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:32:47.988076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:48.747590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:32:56.703595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:57.086064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:32:58.108000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:58.492412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:58.893207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:32:59.399804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:00.016165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:00.461918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:33:30.779454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:31.071773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:31.349828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:31.736173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:32.040741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:33:33.083368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:33:41.843332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:42.246690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:42.596044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:42.959261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:43.443851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:43.873294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:44.279354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:33:45.525106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:45.909234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:46.290323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:46.622920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:46.961411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:47.330822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:47.695310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:48.052371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-16T22:33:48.711014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:49.080550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-16T22:33:57.449704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-16T22:33:57.989027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-16T22:33:58.461840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-16T22:33:58.945986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-16T22:33:49.790924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-16T22:33:50.480892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

ATAPAHAFDPGTEPTITTATTEYCDPCONOXyeartarget
04.58781018.783.6753.575823.9791086.2549.83134.6711.8980.3266381.9522011134.67
14.29321018.384.2353.570923.9511086.1550.05134.6711.8920.4478482.3772011135.10
23.90451018.484.8583.582823.9901086.5550.19135.1012.0420.4514483.7762011135.03
33.74361018.385.4343.580823.9111086.5550.17135.0311.9900.2310782.5052011134.67
43.75161017.885.1823.578123.9171085.9550.00134.6711.9100.2674782.0282011134.67
53.88581017.783.9463.582423.9031086.0549.98134.6711.8680.2347381.7482011134.68
63.66971018.084.1143.580423.8891085.9550.04134.6811.8770.4441284.5922011134.66
73.58921018.283.8673.577723.8761086.0549.88134.6611.8930.7999684.1932011134.65
83.71081018.584.9483.602723.9571086.3549.98134.6511.8700.6899683.9782011132.67
94.82811018.585.3463.515823.4221083.1549.80132.6711.6941.0281082.6542011135.24

Last rows

ATAPAHAFDPGTEPTITTATTEYCDPCONOXyeartarget
367157.21531028.982.3914.779433.2881100.0530.54164.0914.2452.082947.6882015165.41
367165.78841029.285.3604.857033.6441100.0529.19165.4114.3221.795547.2282015167.04
367174.35281029.287.1474.996034.0941100.0527.73167.0414.3781.761847.3702015165.78
367183.76751029.089.6095.073333.4021099.8529.82165.7814.2051.935647.8192015129.86
367193.42181028.791.0033.691122.8591073.5549.78129.8611.5493.673867.7372015117.46
367203.37761028.592.7033.312820.2481057.6550.30117.4610.7835.348866.5502015109.08
367213.62681028.593.2003.166119.0871037.0541.59109.0810.41110.993089.1722015108.79
367224.16741028.694.0363.192319.0161037.6542.28108.7910.34411.144088.8492015107.81
367235.48201028.595.2193.312818.8571038.0543.48107.8110.46211.414096.1472015131.41
367245.88371028.794.2003.983123.5631076.9550.11131.4111.7713.313464.7382015125.41